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+nf-core/deepmodeloptim From 436c6c09b196fc69653294f550dda5ffa5d7168b Mon Sep 17 00:00:00 2001 From: Suzanne Jin Date: Tue, 5 Nov 2024 17:24:09 +0100 Subject: [PATCH 2/7] update readme --- README.md | 21 ++++++++++++++----- assets/nf-core-deepmodeloptim_logo_light.png | Bin 89335 -> 0 bytes 2 files changed, 16 insertions(+), 5 deletions(-) delete mode 100644 assets/nf-core-deepmodeloptim_logo_light.png diff --git a/README.md b/README.md index e4f7050..9e3e897 100644 --- a/README.md +++ b/README.md @@ -17,15 +17,26 @@ [![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmodeloptim-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmodeloptim)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core) + +## 📌 **Quick intro** check out this 👉🏻 [video](https://www.youtube.com/watch?v=dC5p_tXQpEs&list=PLPZ8WHdZGxmVKQga4KE15YVt95i-QXVvE&index=25)! + ## Introduction -**nf-core/deepmodeloptim** is a bioinformatics end-to-end pipeline that performs statistically testing training procedures of machine learning models. +**nf-core/deepmodeloptim** is a bioinformatics end-to-end pipeline designed to facilitate the testing and development of deep learning models for genomics. + +Deep learning model development in natural science is an empirical and costly process. Despite the existence of generic tools for the tuning of hyperparameters and the training of the models, the connection between these procedures and the impact coming from the data is often underlooked, or at least not easily automatized. Indeed, researchers must define a pre-processing pipeline, an architecture, find the best parameters for said architecture and iterate over this process, often manually. + +Leveraging the power of Nextflow (polyglotism, container integration, scalable on the cloud), this pipeline will help users to 1) automatize the testing of the model, 2) gain useful insights with respect to the learning behaviour of the model, and hence 3) accelerate the development. -Deep learning model development in natural science is an empirical and costly process. Users must define a pre-processing pipeline, an architecture, find the best parameters for said architecture and iterate over this process. +## Pipeline summary -Leveraging the power of Nextflow (polyglotism, container integration, scalable on the cloud), we propose STIMULUS, an open-source software built to automatize deep learning model development for genomics. +It takes as input: + - A dataset + - A configuration file to describe the data pre-processing steps to be performed + - An user defined PyTorch model + - A configuration file describing the range of parameters for the PyTorch model -STIMULUS takes as input a user defined PyTorch model, a dataset, a configuration file to describe the pre-processing steps to be performed, and a range of parameters for the PyTorch model. It then transforms the data according to all possible pre-processing steps, finds the best architecture parameters for each of the transformed datasets, performs sanity checks on the models and train a minimal deep learning version for each dataset/architecture. +It then transforms the data according to all possible pre-processing steps, finds the best architecture parameters for each of the transformed datasets, performs sanity checks on the models and train a minimal deep learning version for each dataset/architecture. Those experiments are then compiled into an intuitive report, making it easier for scientists to pick the best design choice to be sent to large scale training. @@ -265,7 +276,7 @@ The `experiment_config` is a mandatory input for the pipeline and can be passed Update the author list --> -nf-core/deepmodeloptim was originally written by Mathys Grapotte. +nf-core/deepmodeloptim was originally written by Mathys Grapotte (@mathysgrapotte). 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z-OY?dt6(o+M@YmjV6s-h(h0GT2=y+s)e53j`-!k*ldzkPAG5(oLvvWaq|$XCbK0hV z3AN3-p;Zd>!#Ia6_<}v5%Z^pczhK}*ITYB&>38OJ1UYTTZ&o*EeQ+3mRj-lJcVQryTzS44|F=q&%FlDJ+j4zj+Nx|Q z8*aNxGmEamYk;=c2~KQxakA3q`bqtd2iD?tvHRCQeNc4te|r9ef3X ztUO_$Ki>s#^b}c2!yCXlxHappaJabxAB9Tn$ZYI%2u^c{ytz745osbfQUmv^4Z^HeLk5mB;*<2`- z=Lei+zow(h#_$gR{>eU3@_%3G|2{|lK@fKOA7AtP$NyS_fB$t9C$j{l#ee@WMfk+c z|Ml_yO>z5QU#j~T(DeV;CtKn4Yv^YD`^V^$b~orY|JM`pAUfW2Nalgs y9XU^kA;~PvB<>r^8W!BKy2Iq From 9b4dfad94309e87111b592ac72d1c22804a5fb63 Mon Sep 17 00:00:00 2001 From: Suzanne Jin Date: Tue, 5 Nov 2024 17:25:25 +0100 Subject: [PATCH 3/7] update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9e3e897..9ab11f7 100644 --- a/README.md +++ b/README.md @@ -276,7 +276,7 @@ The `experiment_config` is a mandatory input for the pipeline and can be passed Update the author list --> -nf-core/deepmodeloptim was originally written by Mathys Grapotte (@mathysgrapotte). +nf-core/deepmodeloptim was originally written by Mathys Grapotte ([@mathysgrapotte](https://github.com/mathysgrapotte)). We thank the following people for their extensive assistance in the development of this pipeline: From 15331a91598d2f54d80cc7c8f2a1aa7d78763b7d Mon Sep 17 00:00:00 2001 From: Suzanne Jin Date: Tue, 5 Nov 2024 17:29:10 +0100 Subject: [PATCH 4/7] fix prettier --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9ab11f7..00cdddc 100644 --- a/README.md +++ b/README.md @@ -17,24 +17,24 @@ [![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmodeloptim-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmodeloptim)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core) - ## 📌 **Quick intro** check out this 👉🏻 [video](https://www.youtube.com/watch?v=dC5p_tXQpEs&list=PLPZ8WHdZGxmVKQga4KE15YVt95i-QXVvE&index=25)! ## Introduction **nf-core/deepmodeloptim** is a bioinformatics end-to-end pipeline designed to facilitate the testing and development of deep learning models for genomics. -Deep learning model development in natural science is an empirical and costly process. Despite the existence of generic tools for the tuning of hyperparameters and the training of the models, the connection between these procedures and the impact coming from the data is often underlooked, or at least not easily automatized. Indeed, researchers must define a pre-processing pipeline, an architecture, find the best parameters for said architecture and iterate over this process, often manually. +Deep learning model development in natural science is an empirical and costly process. Despite the existence of generic tools for the tuning of hyperparameters and the training of the models, the connection between these procedures and the impact coming from the data is often underlooked, or at least not easily automatized. Indeed, researchers must define a pre-processing pipeline, an architecture, find the best parameters for said architecture and iterate over this process, often manually. Leveraging the power of Nextflow (polyglotism, container integration, scalable on the cloud), this pipeline will help users to 1) automatize the testing of the model, 2) gain useful insights with respect to the learning behaviour of the model, and hence 3) accelerate the development. ## Pipeline summary It takes as input: - - A dataset - - A configuration file to describe the data pre-processing steps to be performed - - An user defined PyTorch model - - A configuration file describing the range of parameters for the PyTorch model + +- A dataset +- A configuration file to describe the data pre-processing steps to be performed +- An user defined PyTorch model +- A configuration file describing the range of parameters for the PyTorch model It then transforms the data according to all possible pre-processing steps, finds the best architecture parameters for each of the transformed datasets, performs sanity checks on the models and train a minimal deep learning version for each dataset/architecture. From f5ed9d657b9b2536b25e733750df3200c2efb3e2 Mon Sep 17 00:00:00 2001 From: Suzanne Jin Date: Tue, 5 Nov 2024 17:38:10 +0100 Subject: [PATCH 5/7] add contributors --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 00cdddc..9fa44b3 100644 --- a/README.md +++ b/README.md @@ -278,7 +278,16 @@ The `experiment_config` is a mandatory input for the pipeline and can be passed nf-core/deepmodeloptim was originally written by Mathys Grapotte ([@mathysgrapotte](https://github.com/mathysgrapotte)). -We thank the following people for their extensive assistance in the development of this pipeline: +We would like to thank to all the contributors for their extensive assistance in the development of this pipeline, who include (but not limited to): + +- Alessio Vignoli ([@alessiovignoli](https://github.com/alessiovignoli)) +- Suzanne Jin ([@suzannejin](https://github.com/suzannejin)) +- Luisa Santus ([@luisas](https://github.com/luisas)) +- Jose Espinosa ([@JoseEspinosa](https://github.com/JoseEspinosa)) +- Evan Floden ([@evanfloden](https://github.com/evanfloden)) +- Igor Trujnara ([@itrujnara](https://github.com/itrujnara)) + +Special thanks for the artistic work on the logo to Maxime ([@maxulysse](https://github.com/maxulysse)), Suzanne ([@suzannejin](https://github.com/suzannejin)), Mathys ([@mathysgrapotte](https://github.com/mathysgrapotte)) and, not surprisingly, ChatGPT. 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assets/nf-core-deepmodeloptim_logo_light.png + - docs/images/nf-core-deepmodeloptim_logo_light.png + - docs/images/nf-core-deepmodeloptim_logo_dark.png multiqc_config: false nf_core_version: 3.0.2 org_path: null repository_type: pipeline template: author: Mathys Grapotte - description: nf-core/deepmodeloptim is an end-to-end nextflow based pipeline for - statistically testing training procedures of machine learning models + description: nf-core/deepmodeloptim is an end-to-end pipeline designed to + facilitate the testing and development of deep learning models for genomics. force: false is_nfcore: true name: deepmodeloptim